This document discusses the importance of data integration for new care models like patient-centered medical homes and accountable care organizations. It notes that electronic health records on their own are not sufficient, and that successful models require integrating data across settings to power analytics, clinical decision support, and population health management. The document outlines strategies for organizations, including developing strong data governance, clinical analytics capabilities, and health information exchange infrastructure to share information and coordinate care.
Regulatory reforms and advances in technology are enabling a shift from reactive, provider-centric healthcare to proactive, population-based healthcare management. This involves collecting and analyzing patient data from multiple sources to better understand patient needs, identify patterns, and implement preventative programs and automated interventions. The goal is to improve health outcomes and reduce costs by keeping populations healthy and minimizing expensive medical procedures. Healthcare organizations must adopt new data-driven care models, tools, and workflows to effectively manage population health.
Six Steps to Managing an Infection Control BreachHealth Catalyst
Despite widespread efforts to improve patient safety, infection control breaches still happen at an alarming rate. In order to improve patient safety and prevent infections, healthcare organizations need to have infection control procedures in place and regularly assess protocols and adherence to these policies. In the case of an infection control breach, organizations need to be prepared to act quickly and follow a six-step evaluation procedure outlined by the CDC:
1. Identify the infection control breach.
2. Gather additional data.
3. Notify and involve key stakeholders.
4. Perform a qualitative assessment.
5. Make decisions about patient notification and testing.
6. Handle communications and logistical issues.
This document provides an overview of nursing informatics. It discusses the historical context of nursing informatics from the 1970s to present day. Nursing informatics is defined as integrating nursing science, computer science, and information science to manage and communicate data to support patient care. Key areas covered include competencies, education, roles, skills, evidence-based practice, healthcare technology, socio-technical issues, foundations and committees, and the present and future agenda. The document references several sources and provides images of healthcare technology like alarms and monitoring systems.
Why Clinical Quality Should Be Your Core Business StrategyHealth Catalyst
Over 100 years ago, healing professionals and healthcare itself went through a massive transformation that led us to the models of care delivery that we use today. Dr. Brent James argues that we are now, again, at a once-in-a-century inflection point to change the course of healthcare. Change takes real effort, but provides massive opportunity.
Those changes include a move away from the highly-profitable fee-for-service payment to fee-for-value. An IOM report, published in 2010, substantiated that more than a third of healthcare spending is waste. Pay-for-value aligns financial returns for those who invest in waste elimination. It also requires that clinicians move away from the craft of medicine to the science of medicine, using data and evidence to drive better clinical care.
As the vice president and chief quality officer at Intermountain Healthcare, Dr. James led much of the change that produced Intermountain’s recognized operational and clinical excellence. In this webinar Dr. James educates and inspires all of us to do great work by sharing practical stories of how data has become the critical tool to help healthcare shift from revenue enhancement to clinical quality, which produces the most affordable care.
Learn how to:
- Use data to find variations in both cost and quality of care.
- Standardize care without demotivating underperforming outliers.
- Build a culture of data-driven care providers.
- Develop an improvement strategy that you can start today.
Sought the world over, Dr. James is a recognized expert in this outcomes improvements area. He has championed the standardization of clinical care through data collection and analysis on a wide variety of treatment protocols and complex care processes for more than 20 years.
Achieving Stakeholder Engagement: A Population Health Management ImperativeHealth Catalyst
The document discusses achieving stakeholder engagement in population health management. It states that to improve care in a value-based market, health systems must become competent in PHM but it can be complicated by organizational barriers. It argues that to succeed, health systems need multidisciplinary support across the organization and that earning stakeholder backing relies on real-time, actionable data and analytics to measure effectiveness of improvements.
Analytics-Driven Healthcare: Improving Care, Compliance and CostCognizant
In the face of skyrocketing costs, the healthcare industry is addressing inefficiencies by improving data sharing and collaboration across the industry value chain and applying analytics to improve operations and patient outcomes.
Effective Patient Stratification: Four Solutions to Common HurdlesHealth Catalyst
Accurate patient stratification, the first step of any effective population health strategy, identifies patients who will benefit most from a population health intervention. Successful patient stratification is critical when laying the foundation for any population health initiative, yet many health systems struggle with this step.
Care teams can apply four solutions to overcome common patient stratification hurdles, target the most impactable patients, and carry out population health initiatives:
Consider both the physical and the mental.
Prove and measure return on investment.
Complete data sets.
Transparent, customizable technology.
Continuity of Care Documents: Today’s Top Solution for Healthcare Interoperab...Health Catalyst
While healthcare waits for the expanded data interoperability that FHIR promises, the industry needs an immediate solution for accessing and using disparate data from across the continuum of care. With FHIR potentially several years away, continuity of care documents (CCDs) are the best option for acquiring the ambulatory clinical care data health systems need to close quality gaps today. Because organizations that rely only on claims data to drive quality improvement risk missing out on more that 80 percent of patient information, CCDs are the current must-have answer to interoperability for successful quality improvement.
Regulatory reforms and advances in technology are enabling a shift from reactive, provider-centric healthcare to proactive, population-based healthcare management. This involves collecting and analyzing patient data from multiple sources to better understand patient needs, identify patterns, and implement preventative programs and automated interventions. The goal is to improve health outcomes and reduce costs by keeping populations healthy and minimizing expensive medical procedures. Healthcare organizations must adopt new data-driven care models, tools, and workflows to effectively manage population health.
Six Steps to Managing an Infection Control BreachHealth Catalyst
Despite widespread efforts to improve patient safety, infection control breaches still happen at an alarming rate. In order to improve patient safety and prevent infections, healthcare organizations need to have infection control procedures in place and regularly assess protocols and adherence to these policies. In the case of an infection control breach, organizations need to be prepared to act quickly and follow a six-step evaluation procedure outlined by the CDC:
1. Identify the infection control breach.
2. Gather additional data.
3. Notify and involve key stakeholders.
4. Perform a qualitative assessment.
5. Make decisions about patient notification and testing.
6. Handle communications and logistical issues.
This document provides an overview of nursing informatics. It discusses the historical context of nursing informatics from the 1970s to present day. Nursing informatics is defined as integrating nursing science, computer science, and information science to manage and communicate data to support patient care. Key areas covered include competencies, education, roles, skills, evidence-based practice, healthcare technology, socio-technical issues, foundations and committees, and the present and future agenda. The document references several sources and provides images of healthcare technology like alarms and monitoring systems.
Why Clinical Quality Should Be Your Core Business StrategyHealth Catalyst
Over 100 years ago, healing professionals and healthcare itself went through a massive transformation that led us to the models of care delivery that we use today. Dr. Brent James argues that we are now, again, at a once-in-a-century inflection point to change the course of healthcare. Change takes real effort, but provides massive opportunity.
Those changes include a move away from the highly-profitable fee-for-service payment to fee-for-value. An IOM report, published in 2010, substantiated that more than a third of healthcare spending is waste. Pay-for-value aligns financial returns for those who invest in waste elimination. It also requires that clinicians move away from the craft of medicine to the science of medicine, using data and evidence to drive better clinical care.
As the vice president and chief quality officer at Intermountain Healthcare, Dr. James led much of the change that produced Intermountain’s recognized operational and clinical excellence. In this webinar Dr. James educates and inspires all of us to do great work by sharing practical stories of how data has become the critical tool to help healthcare shift from revenue enhancement to clinical quality, which produces the most affordable care.
Learn how to:
- Use data to find variations in both cost and quality of care.
- Standardize care without demotivating underperforming outliers.
- Build a culture of data-driven care providers.
- Develop an improvement strategy that you can start today.
Sought the world over, Dr. James is a recognized expert in this outcomes improvements area. He has championed the standardization of clinical care through data collection and analysis on a wide variety of treatment protocols and complex care processes for more than 20 years.
Achieving Stakeholder Engagement: A Population Health Management ImperativeHealth Catalyst
The document discusses achieving stakeholder engagement in population health management. It states that to improve care in a value-based market, health systems must become competent in PHM but it can be complicated by organizational barriers. It argues that to succeed, health systems need multidisciplinary support across the organization and that earning stakeholder backing relies on real-time, actionable data and analytics to measure effectiveness of improvements.
Analytics-Driven Healthcare: Improving Care, Compliance and CostCognizant
In the face of skyrocketing costs, the healthcare industry is addressing inefficiencies by improving data sharing and collaboration across the industry value chain and applying analytics to improve operations and patient outcomes.
Effective Patient Stratification: Four Solutions to Common HurdlesHealth Catalyst
Accurate patient stratification, the first step of any effective population health strategy, identifies patients who will benefit most from a population health intervention. Successful patient stratification is critical when laying the foundation for any population health initiative, yet many health systems struggle with this step.
Care teams can apply four solutions to overcome common patient stratification hurdles, target the most impactable patients, and carry out population health initiatives:
Consider both the physical and the mental.
Prove and measure return on investment.
Complete data sets.
Transparent, customizable technology.
Continuity of Care Documents: Today’s Top Solution for Healthcare Interoperab...Health Catalyst
While healthcare waits for the expanded data interoperability that FHIR promises, the industry needs an immediate solution for accessing and using disparate data from across the continuum of care. With FHIR potentially several years away, continuity of care documents (CCDs) are the best option for acquiring the ambulatory clinical care data health systems need to close quality gaps today. Because organizations that rely only on claims data to drive quality improvement risk missing out on more that 80 percent of patient information, CCDs are the current must-have answer to interoperability for successful quality improvement.
Data-Driven Precision Medicine: A Must-Have for the Next-Generation of Person...Health Catalyst
Under a precision medicine approach, clinicians, academics, and pharma and biotech researchers and regulators aim to deliver the right drug for the right patient at the right time. Data, however, can present a challenge to precision medicine goals due to gaps in clinical care, research, and drug development when organizations don’t have the ability to capture and report on relevant real-world data. With the right systems to collect and share clinical and molecular data, the healthcare industry can realize the full benefits of precision medicine.
Achieve Data-Informed Healthcare in Eight StepsHealth Catalyst
Becoming a data-informed healthcare system starts with raw data and ends with meaningful change, driven by raw data. Health systems can follow an eight-step analytics ascension model to transform data into intelligence:
Population Identification and Stratification
Measurement
Data
Information
Knowledge
Insight
Wisdom
Action
Following the analytics ascension model allows improvement teams to avoid feeling overwhelmed, focus on each step, and see how each step fits into the overall objective, allowing health systems to maximize data.
2015 05 01 Pop Health - Laying the Foundation (00000002)Dana Alexander
This document discusses population health management and outlines four key aspects: data control and governance, population management and risk stratification, care management, and patient engagement. It summarizes the challenges of collecting and analyzing large amounts of patient data from electronic health records, developing risk profiles of patient subgroups, implementing targeted care models, and encouraging patient accountability through new technologies. The overall goal is for healthcare organizations to successfully address these areas and achieve true population health management.
Introducing the Health Catalyst Monitor™ Patient Safety Suite Surveillance Mo...Health Catalyst
Unlike the standard post-event reporting process, the Patient Safety Monitor Suite: Surveillance Module is a trigger-based surveillance system, enabled by the unique industry-first technological capabilities of the Health Catalyst Data Operating System platform, including predictive analytic models and AI. Additionally, once listed, the Health Catalyst PSO will create a secure and safe environment where clients can collect and analyze patient safety events to learn and improve, free from fear of litigation. Coupled with patient safety services, an organization’s active all-cause harm patient safety system is fully enabled to deliver measurable and meaningful improvements.
Precision Medicine: Four Trends Make It PossibleHealth Catalyst
When realized, the promise of precision medicine (to specifically tailor treatment to each individual) stands to transform healthcare for the better by delivering more effective, appropriate care. To date, to achieve precision medicine, health systems have faced financial, data management, and interoperability barriers. Current trends in healthcare, however, will give researchers and clinicians the quality and breadth of health data, biological information, and technical sophistication to overcome the challenges to achieving precision medicine.
Four notable trends in healthcare will bolster to growth of precision medicine in the coming years:
Decision support methods harness the power of the human genome.
Healthcare leverages big data analytics and machine learning.
Reimbursement methods incentivize health systems to keep patients well.
Emerging tools enable more data, more interoperability.
Agnostic Analytics Solutions vs. EHRs: Six Reasons EHRs Can’t Deliver True He...Health Catalyst
As enterprisewide analytics demands grow across healthcare, health systems that rely on EHRs from major vendors are hitting limitations in their analytics capabilities. EHR vendors have responded with custom and point-solution tools, but these tend to generate more complications (e.g., multiple data stores and disjointed solutions) than analytics interoperability.
To get value out of existing EHRs while also evolving towards more mature analytics, health systems must partner with an analytics vendor that provides an enterprise data management and analytics platform as well as deep improvement implementation experience. Vendor tools and expertise will help organizations leverage their EHRs to meet population health management and value-based payment goals, as well as pursue some of today’s top healthcare strategic goals:
Growth.
Innovation.
Digitization.
This document provides an outline and overview of content presented on Management Information Systems. The presentation discusses what MIS is, how it has evolved, why it is important, how to organize an MIS, current trends, advantages and limitations. It defines key terms like management, information, data and systems. It also describes the scope of management in healthcare and the management cycle. Additionally, it outlines the components, objectives and evolution of the Health Management Information System in India.
The Population Health Management Market 2015Lifelog Health
Population health management is a problem term because it can mean something different to each person who hears it. However, I believe that the words capture the overall spirit and energy of healthcare reform in a unique way. Providers are thinking big when it comes to a patient’s engagement, responsibility, and preventative care, and they’re leveraging technology to do it. I discuss an overall picture of PHM, present some useful technology, and tell a few PHM stories herein.
Pairing HIE Data with an Analytics Platform: Four Key Improvement CategoriesHealth Catalyst
Population health and value-based payment demand data from multiple sources and multiple organizations. Health systems must access information from across the continuum of care to accurately understand their patients’ healthcare needs beyond the acute-care setting (e.g., reports and results from primary care and specialists). While health system EHRs have a wealth of big-picture data about healthcare delivery (e.g., patient satisfaction, cost, and outcomes), HIEs add the clinical data (e.g., records and transactions) to round out the bigger picture of patient care, as well as the data sharing capabilities needed to disseminate the information.
By pairing HIE capability with an advanced analytics platform, a health system can leverage data to improve processes in four important outcomes improvement areas:
Workflow
Machine learning
Professional services
Data governance
Three Strategies to Deliver Patient-Centered Care in the Next NormalHealth Catalyst
Juggling financial demands, uncertain healthcare legislation, and COVID-19 can distract healthcare leaders from the most important aspect of care—patients. Delivering patient-centered care in this volatile market can be challenging, especially when traditional healthcare methods (e.g., in-person visits) are on hold. These sudden disruptions to routine care have highlighted the importance of keeping patients at the center of care, whether care delivery is in-person or virtual. Health systems can manage competing priorities, adjust to pandemic-induced changes, and deliver patient-centered care by focusing on three strategies:
Improve the patient experience.
Implement the Meaningful Measures Initiative.
Transition in-person visits to virtual.
The document discusses strategies for improving teamwork and patient safety using the TeamSTEPPS framework. It describes how TeamSTEPPS was developed based on over 25 years of research in various high-risk industries. The framework focuses on team structure, leadership, situation monitoring, mutual support, communication, and developing team competencies. Proper teamwork and communication are emphasized as ways to address issues like medical errors during handoffs and reduce preventable adverse events.
How Risk-Bearing Entities Work Together to Succeed at Population HealthHealth Catalyst
Integrating healthcare delivery between risk-bearing entities, such as providers and insurers, is, on the surface, an important step towards population health management and value-based goals. However, even vertically integrated units tend to function separately around patient care. As a result, patients are spread thin between receiving care, navigating insurance, and more—a situation that degrades the patient experience, thwarts optimal outcomes, and interferes with value-based goals. However, some organizations are bridging the gap between healthcare entities to improve quality and decrease costs of caring for at-risk patient populations through a sustainable, collaborative population health model. By joining forces and using analytics to drive decisions and scale programs, truly integrated risk-bearing entities put patients at the center of care, meeting their healthcare needs in a more efficient, cost-effective way.
The document discusses four pressures that are shaping the future of post-acute care: 1) the pressure to serve patients in the lowest-cost setting using telemedicine, telehealth, and mobile technologies; 2) the pressure to avoid readmissions and improve patient compliance; 3) the pressure to remove friction from data and workflow exchange; and 4) the pressure to prove the value of post-acute care. It recommends that post-acute care providers identify challenges, do a reality check on needed changes, and embrace pressures as opportunities to invest in technologies that improve data access, workflows, and care coordination across settings.
Opportunity analysis uses data to identify potential improvement initiatives and quantifies the value of these initiatives—both in terms of patient care benefits and financial impact. This process is an effective way to find unwarranted and costly clinical variation and, in turn, develop strategies to reduce it, improving outcomes and saving costs along the way. Standardizing the opportunity analysis process makes it repeatable and prioritizes actionable opportunities.
Quarterly opportunity analysis should follow four steps:
Kicking off the analysis by getting analysts together to do preliminary analysis and brainstorm.
Engaging with clinicians to identify opportunities and, in the process, get clinician buy in.
Digging deeper into the suggested opportunities to prioritize those that offer the greatest benefits.
Presenting findings to the decision makers.
Providers need to move towards real-time analytics that have become critical to demonstrate their quality of care, as reimbursement by government programs can be contingent upon how providers are measured in “Quality of Care”. For example, the Medicare Access and CHIP Reauthorization Act (MACRA) of 2015, also called the Permanent Doc Fix, changes the way Medicare doctors are reimbursed with the implementation of a merit based incentive. The performance-based pressure is huge, which makes it imperative that every provider consider technology solutions. Read more at https://www.solix.com/solutions/data-driven-solutions/healthcare/
Removing Barriers to Clinician Engagement: Partnerships in Improvement WorkHealth Catalyst
The document summarizes strategies for overcoming barriers to engaging clinicians in quality improvement work. It describes how the University of Kansas Health System partners with clinicians at three levels - local improvement projects, departmental value-based performance, and leadership planning - to achieve system-wide improvements. Examples include reducing COPD readmissions, adopting a less costly acetaminophen, and antibiotic cost savings. The framework aligns clinicians in data-driven improvement work through dedicated performance teams.
Good surfers are the consummate analysts. They dynamically process streams of seemingly unrelated information bypassing lesser opportunities, then strategically selecting the perfect wave.
The ability to tease out genuine opportunities amidst a tumult of noise is a hallmark of great analysts. By viewing these slides you will learn:
- The human elements of a great analyst.
- How to re-frame the role of technology in analysis.
- Healthcare knowledge required to maximize the value of a healthcare analyst.
John Wadsworth's (Senior Vice President of Client Engagement, Health Catalyst) engaging presentation style leverages simple and fun analogies to galvanize key concepts for technical, clinical, and executive audiences alike. Join us as he brings principles from the world of surfing and applies them to healthcare analytics.
Hospital Capacity Management: How to Prepare for COVID-19 Patient SurgesHealth Catalyst
Health system resource strain became an urgent concern early in the COVID-19 pandemic. Hard-hit areas exhausted their hospital beds, ventilators, personal protective equipment, staffing, and other life-saving essentials, while other regions scrambled to prepare for inevitable surges. These resource concerns heightened the need for accurate, localized hospital capacity planning. With additional waves of infection in the summer months following the initial spring 2020 crisis, health systems must continue to forecast resource demands for the foreseeable future. An accurate capacity planning tool uses population demographics, governmental policies, local culture, and the physical environment to predict healthcare resource needs and help health systems prepare for surges in patient demand.
The Digitization of Healthcare: Why the Right Approach Matters and Five Steps...Health Catalyst
While many industries are leveraging digital transformation to accelerate their productivity and quality, healthcare ranks among the least digitized sectors. Healthcare data is largely incomplete when it comes to fully representing a patient’s health and doesn’t adequately support diagnoses and treatment, risk prediction, and long-term health care plans. But even with the obvious urgency for increased healthcare digitization, the industry must raise this trajectory with sensitivity to the impacts on clinicians and patients. The right digital strategy will not only aim for more comprehensive information on patient health, but also leverage data to empower and engage the people involved.
Health systems can follow five guidelines to digitize in a sustainable, impactful way:
Achieve and maintain clinician and patient engagement.
Adopt a modern commercial digital platform.
Digitize the assets (the patients) and the processes.
Understand the importance of data to drive AI insights.
Prioritize data volume.
Computer Information Systems and the Electronic Health RecordRebotto89
Paper-based health records are being replaced by electronic health records (EHRs) to improve patient care. A clinical information system (CIS) is a collection of applications that provides centralized access to patient information across locations. Choosing a CIS requires input from all users and consideration of costs, which can range from $1-2 million for small hospitals to over $1 billion for large hospitals. Ensuring security of patient data and regular system updates are also important factors in selecting and implementing a CIS/EHR.
1) The role of health care data analysts is evolving as the volume of available data grows exponentially. With zettabytes of data being generated, analysts must make sense of both structured and unstructured information.
2) Data analytics can provide insights to improve patient outcomes, lower costs, and enhance the health care experience. Examples show how visualizing data helps health systems better understand utilization and identify at-risk patients.
3) As incentives shift from fee-for-service to value-based models, health systems must transform to focus on population health. Advanced analytics and predictive modeling will be crucial to achieving the goals of better care, lower costs, and improved health.
Data-Driven Precision Medicine: A Must-Have for the Next-Generation of Person...Health Catalyst
Under a precision medicine approach, clinicians, academics, and pharma and biotech researchers and regulators aim to deliver the right drug for the right patient at the right time. Data, however, can present a challenge to precision medicine goals due to gaps in clinical care, research, and drug development when organizations don’t have the ability to capture and report on relevant real-world data. With the right systems to collect and share clinical and molecular data, the healthcare industry can realize the full benefits of precision medicine.
Achieve Data-Informed Healthcare in Eight StepsHealth Catalyst
Becoming a data-informed healthcare system starts with raw data and ends with meaningful change, driven by raw data. Health systems can follow an eight-step analytics ascension model to transform data into intelligence:
Population Identification and Stratification
Measurement
Data
Information
Knowledge
Insight
Wisdom
Action
Following the analytics ascension model allows improvement teams to avoid feeling overwhelmed, focus on each step, and see how each step fits into the overall objective, allowing health systems to maximize data.
2015 05 01 Pop Health - Laying the Foundation (00000002)Dana Alexander
This document discusses population health management and outlines four key aspects: data control and governance, population management and risk stratification, care management, and patient engagement. It summarizes the challenges of collecting and analyzing large amounts of patient data from electronic health records, developing risk profiles of patient subgroups, implementing targeted care models, and encouraging patient accountability through new technologies. The overall goal is for healthcare organizations to successfully address these areas and achieve true population health management.
Introducing the Health Catalyst Monitor™ Patient Safety Suite Surveillance Mo...Health Catalyst
Unlike the standard post-event reporting process, the Patient Safety Monitor Suite: Surveillance Module is a trigger-based surveillance system, enabled by the unique industry-first technological capabilities of the Health Catalyst Data Operating System platform, including predictive analytic models and AI. Additionally, once listed, the Health Catalyst PSO will create a secure and safe environment where clients can collect and analyze patient safety events to learn and improve, free from fear of litigation. Coupled with patient safety services, an organization’s active all-cause harm patient safety system is fully enabled to deliver measurable and meaningful improvements.
Precision Medicine: Four Trends Make It PossibleHealth Catalyst
When realized, the promise of precision medicine (to specifically tailor treatment to each individual) stands to transform healthcare for the better by delivering more effective, appropriate care. To date, to achieve precision medicine, health systems have faced financial, data management, and interoperability barriers. Current trends in healthcare, however, will give researchers and clinicians the quality and breadth of health data, biological information, and technical sophistication to overcome the challenges to achieving precision medicine.
Four notable trends in healthcare will bolster to growth of precision medicine in the coming years:
Decision support methods harness the power of the human genome.
Healthcare leverages big data analytics and machine learning.
Reimbursement methods incentivize health systems to keep patients well.
Emerging tools enable more data, more interoperability.
Agnostic Analytics Solutions vs. EHRs: Six Reasons EHRs Can’t Deliver True He...Health Catalyst
As enterprisewide analytics demands grow across healthcare, health systems that rely on EHRs from major vendors are hitting limitations in their analytics capabilities. EHR vendors have responded with custom and point-solution tools, but these tend to generate more complications (e.g., multiple data stores and disjointed solutions) than analytics interoperability.
To get value out of existing EHRs while also evolving towards more mature analytics, health systems must partner with an analytics vendor that provides an enterprise data management and analytics platform as well as deep improvement implementation experience. Vendor tools and expertise will help organizations leverage their EHRs to meet population health management and value-based payment goals, as well as pursue some of today’s top healthcare strategic goals:
Growth.
Innovation.
Digitization.
This document provides an outline and overview of content presented on Management Information Systems. The presentation discusses what MIS is, how it has evolved, why it is important, how to organize an MIS, current trends, advantages and limitations. It defines key terms like management, information, data and systems. It also describes the scope of management in healthcare and the management cycle. Additionally, it outlines the components, objectives and evolution of the Health Management Information System in India.
The Population Health Management Market 2015Lifelog Health
Population health management is a problem term because it can mean something different to each person who hears it. However, I believe that the words capture the overall spirit and energy of healthcare reform in a unique way. Providers are thinking big when it comes to a patient’s engagement, responsibility, and preventative care, and they’re leveraging technology to do it. I discuss an overall picture of PHM, present some useful technology, and tell a few PHM stories herein.
Pairing HIE Data with an Analytics Platform: Four Key Improvement CategoriesHealth Catalyst
Population health and value-based payment demand data from multiple sources and multiple organizations. Health systems must access information from across the continuum of care to accurately understand their patients’ healthcare needs beyond the acute-care setting (e.g., reports and results from primary care and specialists). While health system EHRs have a wealth of big-picture data about healthcare delivery (e.g., patient satisfaction, cost, and outcomes), HIEs add the clinical data (e.g., records and transactions) to round out the bigger picture of patient care, as well as the data sharing capabilities needed to disseminate the information.
By pairing HIE capability with an advanced analytics platform, a health system can leverage data to improve processes in four important outcomes improvement areas:
Workflow
Machine learning
Professional services
Data governance
Three Strategies to Deliver Patient-Centered Care in the Next NormalHealth Catalyst
Juggling financial demands, uncertain healthcare legislation, and COVID-19 can distract healthcare leaders from the most important aspect of care—patients. Delivering patient-centered care in this volatile market can be challenging, especially when traditional healthcare methods (e.g., in-person visits) are on hold. These sudden disruptions to routine care have highlighted the importance of keeping patients at the center of care, whether care delivery is in-person or virtual. Health systems can manage competing priorities, adjust to pandemic-induced changes, and deliver patient-centered care by focusing on three strategies:
Improve the patient experience.
Implement the Meaningful Measures Initiative.
Transition in-person visits to virtual.
The document discusses strategies for improving teamwork and patient safety using the TeamSTEPPS framework. It describes how TeamSTEPPS was developed based on over 25 years of research in various high-risk industries. The framework focuses on team structure, leadership, situation monitoring, mutual support, communication, and developing team competencies. Proper teamwork and communication are emphasized as ways to address issues like medical errors during handoffs and reduce preventable adverse events.
How Risk-Bearing Entities Work Together to Succeed at Population HealthHealth Catalyst
Integrating healthcare delivery between risk-bearing entities, such as providers and insurers, is, on the surface, an important step towards population health management and value-based goals. However, even vertically integrated units tend to function separately around patient care. As a result, patients are spread thin between receiving care, navigating insurance, and more—a situation that degrades the patient experience, thwarts optimal outcomes, and interferes with value-based goals. However, some organizations are bridging the gap between healthcare entities to improve quality and decrease costs of caring for at-risk patient populations through a sustainable, collaborative population health model. By joining forces and using analytics to drive decisions and scale programs, truly integrated risk-bearing entities put patients at the center of care, meeting their healthcare needs in a more efficient, cost-effective way.
The document discusses four pressures that are shaping the future of post-acute care: 1) the pressure to serve patients in the lowest-cost setting using telemedicine, telehealth, and mobile technologies; 2) the pressure to avoid readmissions and improve patient compliance; 3) the pressure to remove friction from data and workflow exchange; and 4) the pressure to prove the value of post-acute care. It recommends that post-acute care providers identify challenges, do a reality check on needed changes, and embrace pressures as opportunities to invest in technologies that improve data access, workflows, and care coordination across settings.
Opportunity analysis uses data to identify potential improvement initiatives and quantifies the value of these initiatives—both in terms of patient care benefits and financial impact. This process is an effective way to find unwarranted and costly clinical variation and, in turn, develop strategies to reduce it, improving outcomes and saving costs along the way. Standardizing the opportunity analysis process makes it repeatable and prioritizes actionable opportunities.
Quarterly opportunity analysis should follow four steps:
Kicking off the analysis by getting analysts together to do preliminary analysis and brainstorm.
Engaging with clinicians to identify opportunities and, in the process, get clinician buy in.
Digging deeper into the suggested opportunities to prioritize those that offer the greatest benefits.
Presenting findings to the decision makers.
Providers need to move towards real-time analytics that have become critical to demonstrate their quality of care, as reimbursement by government programs can be contingent upon how providers are measured in “Quality of Care”. For example, the Medicare Access and CHIP Reauthorization Act (MACRA) of 2015, also called the Permanent Doc Fix, changes the way Medicare doctors are reimbursed with the implementation of a merit based incentive. The performance-based pressure is huge, which makes it imperative that every provider consider technology solutions. Read more at https://www.solix.com/solutions/data-driven-solutions/healthcare/
Removing Barriers to Clinician Engagement: Partnerships in Improvement WorkHealth Catalyst
The document summarizes strategies for overcoming barriers to engaging clinicians in quality improvement work. It describes how the University of Kansas Health System partners with clinicians at three levels - local improvement projects, departmental value-based performance, and leadership planning - to achieve system-wide improvements. Examples include reducing COPD readmissions, adopting a less costly acetaminophen, and antibiotic cost savings. The framework aligns clinicians in data-driven improvement work through dedicated performance teams.
Good surfers are the consummate analysts. They dynamically process streams of seemingly unrelated information bypassing lesser opportunities, then strategically selecting the perfect wave.
The ability to tease out genuine opportunities amidst a tumult of noise is a hallmark of great analysts. By viewing these slides you will learn:
- The human elements of a great analyst.
- How to re-frame the role of technology in analysis.
- Healthcare knowledge required to maximize the value of a healthcare analyst.
John Wadsworth's (Senior Vice President of Client Engagement, Health Catalyst) engaging presentation style leverages simple and fun analogies to galvanize key concepts for technical, clinical, and executive audiences alike. Join us as he brings principles from the world of surfing and applies them to healthcare analytics.
Hospital Capacity Management: How to Prepare for COVID-19 Patient SurgesHealth Catalyst
Health system resource strain became an urgent concern early in the COVID-19 pandemic. Hard-hit areas exhausted their hospital beds, ventilators, personal protective equipment, staffing, and other life-saving essentials, while other regions scrambled to prepare for inevitable surges. These resource concerns heightened the need for accurate, localized hospital capacity planning. With additional waves of infection in the summer months following the initial spring 2020 crisis, health systems must continue to forecast resource demands for the foreseeable future. An accurate capacity planning tool uses population demographics, governmental policies, local culture, and the physical environment to predict healthcare resource needs and help health systems prepare for surges in patient demand.
The Digitization of Healthcare: Why the Right Approach Matters and Five Steps...Health Catalyst
While many industries are leveraging digital transformation to accelerate their productivity and quality, healthcare ranks among the least digitized sectors. Healthcare data is largely incomplete when it comes to fully representing a patient’s health and doesn’t adequately support diagnoses and treatment, risk prediction, and long-term health care plans. But even with the obvious urgency for increased healthcare digitization, the industry must raise this trajectory with sensitivity to the impacts on clinicians and patients. The right digital strategy will not only aim for more comprehensive information on patient health, but also leverage data to empower and engage the people involved.
Health systems can follow five guidelines to digitize in a sustainable, impactful way:
Achieve and maintain clinician and patient engagement.
Adopt a modern commercial digital platform.
Digitize the assets (the patients) and the processes.
Understand the importance of data to drive AI insights.
Prioritize data volume.
Computer Information Systems and the Electronic Health RecordRebotto89
Paper-based health records are being replaced by electronic health records (EHRs) to improve patient care. A clinical information system (CIS) is a collection of applications that provides centralized access to patient information across locations. Choosing a CIS requires input from all users and consideration of costs, which can range from $1-2 million for small hospitals to over $1 billion for large hospitals. Ensuring security of patient data and regular system updates are also important factors in selecting and implementing a CIS/EHR.
1) The role of health care data analysts is evolving as the volume of available data grows exponentially. With zettabytes of data being generated, analysts must make sense of both structured and unstructured information.
2) Data analytics can provide insights to improve patient outcomes, lower costs, and enhance the health care experience. Examples show how visualizing data helps health systems better understand utilization and identify at-risk patients.
3) As incentives shift from fee-for-service to value-based models, health systems must transform to focus on population health. Advanced analytics and predictive modeling will be crucial to achieving the goals of better care, lower costs, and improved health.
The document discusses trends in healthcare data and analytics. It covers four main topics: 1) industry dynamics and business priorities in healthcare are driving a focus on value-based care and lower costs while engaging patients, 2) healthcare is experiencing a big data explosion from sources like EMRs and devices, 3) key trends include predictive analytics, cognitive computing, and value-based care, and 4) opportunities exist in population health and clinical decision support while challenges include lack of integration and security concerns.
PHM is a systematic way of gathering, analysing and managing at-risk patients’ data through tools such as Utilization Management, Case Management, Disease Management, Portals etc.
Healthcare organizations need to have technological capabilities within their care delivery processes to effectively use data to manage the cost and quality of care. To pursue more aggressive risk-based reimbursement models, these capabilities need to be expanded strategically and proportionately.
Explains about Evolution of IT in Healthcare, how analytics can make a difference and evolution of IT in healtcare. For more information visit: http://www.transformhealth-it.org/
IT trends in the US healthcare sector are driven by incentives to cut costs while improving care integration. Spending on healthcare IT is projected to grow from $54 billion in 2010 to $80 billion in 2017. Emerging technologies like mobile health, bring your own device (BYOD), big data analytics, and interoperable electronic health records aim to enhance care delivery and lower costs. Adoption of standards like ICD-10, HL7, and meaningful use incentives also promote IT-enabled transformation across providers, payers, and life sciences organizations.
What eHealth strategies work and do not work, and what should be implemented to effectively meet these healthcare “transformational” imperatives?. Crawford J. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)
Health systems recognize the potential of digital health but e-health programs have had modest returns. Ambitious initiatives focus on providing clinicians information but struggle with legacy systems that impede data integration. The solution is a digital services platform that holds healthcare data and optimizes access through APIs and services for identity, access and consent management. This platform could serve as an innovation ecosystem for third-party digital health services and advanced by health systems. It could revolutionize health services and help bend the cost curve through contextualized information, ushering in an era of "Healthcare 3.0."
This document discusses collaboration strategies between the Chief Medical Information Officer (CMIO) and Chief Medical Officer (CMO) at Kings County Hospital. It provides background on Kings County Hospital, which is a 650-bed academic and tertiary hospital within the New York City Health & Hospitals Corporation public hospital system. It then outlines how the CMIO and CMO roles have evolved to focus more on analytics, reporting, and using health IT to improve quality, safety, and achieve regulatory and reform goals like accountable care. Specific examples of collaborations around computerized physician order entry, clinical decision support, and reducing hospital-acquired infections are also provided.
5 Things to Know About the Clinical Analytics Data Management Challenge - Ext...Michael Dykstra
5 Things to Know About the Clinical Analytics Data Management Challenge - Extracting Real Benefit From Your EHR Data
The EHR revolution has created immense promise for improved patient outcomes and reduced costs but most healthcare organizations are struggling to experience significant benefits. The power of Applied Clinical Analytics lies in a simple but powerful concept: the importance of focusing on the accuracy and availability of the underlying data, first and foremost.
Going Beyond the EMR for Data-driven Insights in HealthcarePerficient, Inc.
Join Dr. Marcie Stoshak-Chavez, MD, FACEP, Director of Healthcare Strategic Advisory Services at Perficient and Mr. J.D. Whitlock, Director of Clinical & Business Intelligence at Catholic Health Partners to learn how analytics is being used to measure and monitor performance and provide service-line directors and financial administrators with reporting and analysis that enhances clinical care processes and business operations.
Learn how clinicians and administrators armed with the data-driven insights from the EMR and beyond can:
Derive meaningful insights for care delivery by analyzing clinical, financial and operational data
Collaborate more effectively and improve quality of care by securely sharing insights among providers
Meaningfully measure and understand performance across key Federally mandated measures and take prescribed action
Stay on top of shifts in regulatory policy that impact reimbursements and quality requirements
Digital healthcare technologies are transforming healthcare delivery globally. Companies are developing technologies like mobile apps, big data analytics, and smart medical devices to improve patient monitoring and outcomes. These digital innovations extract insights from medical data to enhance healthcare provisioning, reduce costs, and support preventative care and remote patient monitoring. Emerging areas like bioinformatics and medical analytics utilize big data to provide actionable clinical insights.
Key Takeaways from the first IDC Pan European Healthcare Summit . Post event ...Silvia Piai
This slide deck summarizes the key takeaways from the first Pan European Healthcare Executive Event. Focused on the three themes of the Summit ( Personalization,Integration and Industrialization), the Summit has explored the different dimensions in which ICT is an enabler of a new business model for sustainable healthcare in Europe
Next generation predictive models leveraging real patient data from electronic health records will transform care management by enabling individualized, automated, and proactive care. Key factors enabling this transformation include meaningful use of EHRs to capture structured clinical data, more granular ICD-10 coding, and using real-time EHR data to continuously enhance predictive models and tightly couple model insights with care processes and clinical decision support. This will allow identifying high-risk complex patients early and providing optimized individualized guidelines that can improve outcomes while reducing costs compared to general guidelines.
Healthcare data and its impact upon the patient care decision process via accurate, real-time, reliable data from disparate sources is creating a digital health revolution. Data-driven healthcare is beginning to have a huge impact addressing the challenges of every provider, through efficient handling of huge volumes of patient care data.
Although there have been enormous strides made in the area of health information technology, most developers and users feel frustrated by the pace of change. This new institute will drive Strategy, Innovation and Design for Health ICT
Paperless Hospitals Dr Dev Taneja 3rd June2012DrDevTaneja
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(Thursday, 4.15, Panel)
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Data Integration – The Key To Successfully Utilizing Information
1. Data Integration – The Key to Successfully
Utilizing Information for Point of Care and for
Population Health
Charles DeShazer, MD
VP, Quality, Medical Informatics &
Transformation
Dean Health System
2. Overview
Implications of New Emerging Care Models
Implications of Medical Care Evolution
Implications of Government Interventions
Government Vision of HIT
Critical Success Factor – Data Integration &
Distribution
“Big Data” Analytics
Key Strategies & Tactics
3. Overview
Implications of New Emerging Care Models
Implications of Medical Care Evolution
Implications of Government Interventions
Government Vision of HIT
Critical Success Factor – Data Integration &
Distribution
“Big Data” Analytics
Key Strategies & Tactics
4. New Care Models
Patient-Centered Medical Home
Health care model that aims to provide structured,
proactive and coordinated care for patients.
Accountable Care Organizations
Group of health care providers (e.g. primary care
physicians, specialists and hospitals) that have entered
into a formal arrangement to assume collective
responsibility for the cost and quality of care of a specific
group of patients and that receive financial incentives to
improve the quality and efficiency of health care.
Payment Driven Models
Bundled payments
Pay for Performance
Case rates
Capitation
5. ACO model represents a shift of COST RISK to
Providers through payment mechanisms…
6. Population vs. Costs vs. Interventions
Example of 100,000 People in a Population
% of
% of Cost
Population
Complex Case Management
1% 1000
25%
Lives
Disease/Demand
14% 14,000 Lives Management 50%
15% 15,000 Lives Health 15%
Mgmt
70% 70,000 Lives 10%
Approximately 75% of costs are due to chronic co
7. Healthcare Information Technology (HIT)
Requirements
PCMH ACOs
Care Coordination Cross Continuum
Chronic Disease Medical Management
Management & Member Engagement
Complex Case Clinical Information
Management Exchange
Population Health Quality & Performance
Management (esp Reporting
registries) Predictive Modeling &
Patient Engagement & Analytics
Activation
Administrative and
Evidence-based Financial Risk
Medical Practice Management systems
Real-time connectivity
8. EHR is necessary but not
sufficient
In "Associations Between Structural Capabilities of Primary Care
Practices and Performance on Selected Quality Measures," Mark
Friedberg MD, and colleagues examine how a range of primary
care practice attributes, including having an EMR, may impact
physician performance on quality metrics.
The research profiled 307 practices in Massachusetts across
2007.
Across the practice characteristics and HEDIS metrics, the
attributes correlated to a practice's higher-quality performance on
diabetes and prevention metrics included: having an EMR,
frequent meetings to discuss practice quality performance, and
physician awareness of patient experience. EMRs were
specifically associated with higher performance on two diabetes
metrics (eye exams and nephropathy monitoring) and three
prevention metrics (breast cancer, colorectal cancer, and
chlamydia).
Key insight: The key transformative aspect of the EMR's role
in the practice was shown to be providing information to
support decision-making--not just serving as a repository
Friedberg, M., et al, Annals of Internal Medicine, 2009; 151:456-463
for data.
9. Overview
Implications of New Emerging Care Models
Implications of Medical Care Evolution
Implications of Government Interventions
Government Vision of HIT
Critical Success Factor – Data Integration &
Distribution
“Big Data” Analytics
Key Strategies & Tactics
10. Continued Evolution of the Medical
Care …
Genomics
New Technology
Aging Population
11. Overview
Implications of New Emerging Care Models
Implications of Medical Care Evolution
Implications of Government Interventions
Government Vision of HIT
Critical Success Factor – Data Integration &
Distribution
“Big Data” Analytics
Key Strategies & Tactics
12. Meaningful Use & ICD-10
Meaningful Use
Driving increased adoption of EHRs in-patient and ambulatory
Penalty starts if not “meaningful user” by 2015
Infrastructure for ONC vision and robust Clinical Decision Support
(CDS)
ICD-10
One of the most comprehensive regulatory changes in the history of
healthcare in the US
Unlike MU, it is an unfunded regulatory event
Replaces 30 year old ICD-9-CM, which is outdated and lacks clinical
granularity
Provides granularity to diagnostic information that should
greatly enhance predictive models
Improved ability to specify and measure healthcare services
Enable better integration of predictive modeling and clinical
decision support
Richer data structures for research
13. ICD-10 Asthma Codes More granular clinical
information will
enhance predictive
models as well as
enable real-time
program referrals
especially when
followed serially and
combined with other
data.
14. Overview
Implications of New Emerging Care Models
Implications of Medical Care Evolution
Implications of Government Interventions
Government Vision of HIT
Critical Success Factor – Data Integration &
Distribution
“Big Data” Analytics
Key Strategies & Tactics
15.
16. Overview
Implications of New Emerging Care Models
Implications of Medical Care Evolution
Implications of Government Interventions
Government Vision of HIT
Critical Success Factor – Data Integration &
Distribution
“Big Data” Analytics
Key Strategies & Tactics
17. Data Management Lifecycle
Data Collection (QA) Data Extraction (ETL)
•Avoid GIGO •Critical Integration Step
• Collection workflows •Data Governance &
• Clarity of where to enter Master Data Management
data to be reportable •Data warehouse & marts
• Coding consistency and • Single source of truth
conventions (ICD-10) • Create Predictive &
Analytic Models
•Establish accountability •Leverage Analytics for
and feedback mechanisms Insights that drive
• Link with Lean efforts decisions and processes
• Identify gaps and new •Testing and Validation
requirements • Formatting for ACTION
•Learn from reports & • Visualization of data
change collection process • Delivery medium incl. CDS
Improvement Initiatives Information Delivery
18. Key Technical Infrastructure to support the
PCMH
EHR is necessary but not sufficient. The next level of quality management will
require an INTEGRATED Health Information Technology (HIT)
“ecosystem” especially a robust analytic infrastructure. Standalone EHR
may not be able to provide all of these functions.
Focus Area Key Technical Infrastructure
Care Coordination HIE, Workflow Management, Shared Care Plan,
Referral tracking
Chronic Condition Management CRM, Workflow Management, Shared Care
& Complex Care Management Plan, Predictive Modeling, CDS, Telehealth,
Registries
Population Health Management CRM, HRA, Predictive Modeling, Workflow
Management, CDS, Population analytics,
Registries
Patient Engagement & Activation CRM, Shared Decision Making, Telehealth, PHR
Evidence-Based Medicine CDS, Workflow Management, Population
Practice analytics
Real-Time Connectivity HIE, Telehealth, mobile technology, unified
messaging
20. Overview
Implications of New Emerging Care Models
Implications of Medical Care Evolution
Implications of Government Interventions
Government Vision of HIT
Critical Success Factor – Data Integration &
Distribution
“Big Data” Analytics
Key Strategies & Tactics
21. “Big Data” Analytics
Recent IDC research on digital data indicates that in 2010, the amount of digital
information in the world reached beyond a zettabyte in size. That's one trillion gigabytes
of information. To put that in perspective, a blogger at Cisco Systems noted that a
zettabyte is roughly the size of 125 billion 8GB iPods fully loaded.
The increasing velocity, variety and complexity of data is overwhelming traditional
datawarehouse tools, techniques and infrastructure.
Healthcare has a particular need to manage data well as EHRs become common, use of
devices increase, integration of multimedia and imaging becomes important, integration
with social networking resources becomes useful and genomics data becomes essential
for decision-making.
New high performance hardware, software and techniques are emerging to address this
issue called “Big Data” Analytics.
Gartner contends that terms like "big data," "real-time data" and "linked data" signal a
new era in which the economics of data (not the economics of applications, software or
hardware) will drive competitive advantage.
What does this mean? It doesn't matter which EHR you have from a competitive
standpoint. Competitive advantage will come from (1) the quality of the data you
collect, (2) how you integrate the data and provide analytics to drive insights and
(3) how well you use these insights to drive customer experience/relationships and
business results.
22. Overview
Implications of New Emerging Care Models
Implications of Medical Care Evolution
Implications of Government Interventions
Government Vision of HIT
Critical Success Factor – Data Integration &
Distribution
“Big Data” Analytics
Key Strategies & Tactics
23. Leverage Meaningful Use as a Springboard
Criteria Opportunity
Problem List Define system-wide standards and policies,
improve accuracy of documentation, infrastructure
for CDS, use for shared care plan
AVS & PHR Enhance quality, consistency and usefulness of
content (esp. for chronic condition management),
fully operationalize PHR, enhance patient
engagement, use for shared care plan, leverage to
engage family & caregivers
Medication List Improve medication reconciliation and
management of transitions of care.
Patient Lists & Enhance analytics, create robust registries and
Structured Data dashboards, infrastructure for CDS
Clinical Decision Create governance structure, establish standards,
Support focus them on key areas of improvement
opportunity, avoid alert fatigue
Quality Measures Expect to be held accountable for results, create
24. Key Tactics & Strategies
Implement & Optimize your EHR
Consider "Big Data" Analytics
Develop an effective data governance and master data
management strategy
Develop your Clinical Analytics unit
Enhance your CDS infrastructure
Decide on and commit the organization to an improvement
methodology, this is the cultural change tool
Invest in workflow optimization (good use for Lean
techniques)
Docs should manage by exception
Consider how you will create a “Shared Care Plan” (SCP)
to ensure all providers and the patient are on the same
page
Create enterprise and community infrastructure for health
information exchange and CRM at the ACO level 24
Develop approaches to activate consumer/patient & family
Editor's Notes
Building the HIT Infrastructure for the Patient Centered Medical Home and Accountable Care OrganizationsThe EHR is necessary but not sufficientUnderstand the HIT implications of the new emerging care models. HIT not only closes critical gaps in how care is delivered but will be essential to enabling higher levels of competitive performance. The EHR is essential but must be optimized and integrated into a complete HIT “ecosystem” and transformed culture to deliver on its promise. Impact of current and expected government initiatives HIT requirements for the PCMH and ACOs Best practices for managing the data lifecycle Understand “Big Data” analytics Key strategies and tactics to implement the necessary HIT infrastructure
Define how to leverage and transformAccording to the U.S. Department of Health and Human Services, the current system, ICD-9-CM, does not provide the necessary detail for patients’ medical conditions or the procedures and services performed on hospitalized patients. ICD-9-CM is 30 years old, has outdated and obsolete terminology, uses outdated codes that produce inaccurate and limited data, and is inconsistent with current medical practice. It cannot accurately describe the diagnoses and inpatient procedures of care delivered in the 21st century. ICD-10-CM/PCS Incorporates much greater specificity and clinical information, which results in:Improved ability to measure health care servicesIncreased sensitivity when refining grouping and reimbursement methodologiesEnhanced ability to conduct public health surveillance; and decreased need to include supporting documentation with claimsIncludes updated medical terminology and classification of diseasesProvides codes to allow comparison of mortality and morbidity dataProvides better data for:Measuring care furnished to patientsDesigning payment systemsProcessing claimsMaking clinical decisionsTracking public healthIdentifying fraud and abusePerformance improvement plansConducting research In order for organizations to be successful with implementing ICD-10-CM/PCS and also meeting the criteria for meaningful use of electronic health records, physician documentation must be thorough. Clinical data documented through patient history and physical exams, clinical treatments, medication therapy, surgical procedures, and clinical outcomes should be documented thoroughly. Although Stage 1 of meaningful use calls for much less criteria than Stage 2 for physician documentation, the best practice should be to institute improved documentation now. The level of physician documentation influences quality measuring and reporting, what types of clinical information will be available when Health Information Exchange data is provided, and overall clinical performance improvement plans for the organization. So there is a direct link between improving physician documentation to prepare for ICD-10, meeting the criteria for the various stages of meaningful use, and measuring quality care for improvement purposes.